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A privacy-preserving multidimensional data aggregation scheme with secure query processing for smart grid

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Abstract

A smart meter is a critical component of the smart grid that collects data and reports to electricity companies in several minutes or seconds. The real-time power consumption of smart meters helps electricity companies to provide reliable services but can expose smart meter customers’ privacy. Therefore, aggregation of encrypted power consumption of smart meters is used to protect privacy. To meet the requirements, we propose a Privacy-Preserving Multidimensional Data Aggregation (PP-MDA) scheme for smart grid. Paillier cryptosystem is used to aggregate the encrypted power consumption at the fog and cloud sites. In addition to aggregation, PP-MDA scheme is also used for secure query processing; ID-based and time-based. Simulation results show the superiority of the PP-MDA scheme with existing solutions in terms of communication cost and features.

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References

  1. Hou W, Ning Z, Guo L, Zhang X (2017) Temporal, functional and spatial big data computing framework for large-scale smart grid. IEEE Trans Emerg Top Comput 7(3):369–379

    Article  Google Scholar 

  2. Gai K, Yulu W, Zhu L, Lei X, Zhang Y (2019) Permissioned blockchain and edge computing empowered privacy-preserving smart grid networks. IEEE Internet Things J 6(5):7992–8004

    Article  Google Scholar 

  3. Wang K, Jun Yu, Yan Yu, Qian Y, Zeng D, Guo S, Xiang Y, Jinsong W (2017) A survey on energy internet: architecture, approach, and emerging technologies. IEEE Syst J 12(3):2403–2416

    Article  Google Scholar 

  4. Wallace N, Castro D (2017) The state of data innovation in the EU. Center for Data Innovation

  5. Jiang R, Rongxing L, Luo J, Lai C, Shen X (2015) Efficient self-healing group key management with dynamic revocation and collusion resistance for SCADA in smart grid. Secur Commun Netw 8(6):1026–1039

    Article  Google Scholar 

  6. Kaneriya S, Tanwar S, Nayyar A, Verma JP, Tyagi S, Kumar N, Obaidat MS, Rodrigues Joel JPC(2018) Data consumption-aware load forecasting scheme for smart grid systems. In: 2018 IEEE Globecom Workshops (GC Wkshps), pp 1-6. IEEE

  7. Zheng KN, Guo J, Xinye Z (2016) Effects of demand side management on Chinese household electricity consumption: empirical findings from Chinese household survey. Energy Policy 95:113–125

    Article  Google Scholar 

  8. Sarah D (2006) The Effectiveness of Feedback on Energy Consumption: a Review for DEFRA of the Literature on Metering. University of Oxford, Billing and Direct Displays. environmental Change institute

  9. McDaniel P, McLaughlin S (2009) Security and privacy challenges in the smart grid. IEEE Secur Priv 7(3):75–77

    Article  Google Scholar 

  10. Cunsolo VD, Distefano S, Puliafito A, Scarpa ML (2010) GS\(^3\): a grid storage system with security features. J Grid Comput 8(3):391–418

    Article  Google Scholar 

  11. Choo K-KR (2011) The cyber threat landscape: challenges and future research directions. Comput Secur 30(8):719–731

    Article  Google Scholar 

  12. Shateri M, Messina F, Piantanida P, Labeau F (2020) Real-time privacy-preserving data release for smart meters. IEEE Trans Smart Grid 11(6):5174–5183

    Article  Google Scholar 

  13. John JS (2013) Siemens eMeter push smart meter data and analytics to the cloud, [Online]. Available: http://www.greentechmedia.com/articles/read/siemens-emeter-push-smartmeter-data-and-analytics-to-the-cloud

  14. Kumari A, Sudeep T, Sudhanshu T, Neeraj K, Mohammad SO, Rodrigues JJPC (2019) Fog computing for smart grid systems in the 5G environment: challenges and solutions. IEEE Wirel Commun 26(3):47–53

    Article  Google Scholar 

  15. Priyadarshini R, Malarvizhi N, Neeba EA (2019) A study on capabilities and challenges of fog computing. In: Novel Practices and Trends in Grid and Cloud Computing, pp 249–273. IGI Global

  16. Lu R, Xiaohui Liang X, Li XL, Shen X (2012) EPPA: an efficient and privacy-preserving aggregation scheme for secure smart grid communications. IEEE Trans Parallel Distrib Syst 23(9):1621–1631

    Article  Google Scholar 

  17. Boudia ORM, Senouci SM, Feham M (2017) Elliptic curve-based secure multidimensional aggregation for smart grid communications. IEEE Sens J 17(23):7750–7757

    Article  Google Scholar 

  18. Chen L, Rongxing L, Cao Z (2015) PDAFT: A privacy-preserving data aggregation scheme with fault tolerance for smart grid communications. Peer-to-Peer Netw Appl 8(6):1122–1132

    Article  Google Scholar 

  19. Zhang Z, Dong M, Zhu L, Guan Z, Chen R, Rixin X, Ota K (2017) Achieving privacy-friendly storage and secure statistics for smart meter data on outsourced clouds. IEEE Trans Cloud Comput 7(3):638–649

    Article  Google Scholar 

  20. Badra M, Zeadally S (2017) Lightweight and efficient privacy-preserving data aggregation approach for the smart grid. Ad Hoc Netw 64:32–40

    Article  Google Scholar 

  21. Abdallah A, Shen XS (2016) A lightweight lattice-based homomorphic privacy-preserving data aggregation scheme for smart grid. IEEE Trans Smart Grid 9(1):396–405

    Article  Google Scholar 

  22. Lyu L, Nandakumar K, Rubinstein B, Jin J, Bedo J, Palaniswami M (2018) PPFA: privacy preserving fog-enabled aggregation in smart grid. IEEE Trans Industr Inf 14(8):3733–3744

    Article  Google Scholar 

  23. Ming Y, Zhang X, Shen X (2019) Efficient privacy-preserving multi-dimensional data aggregation scheme in smart grid. IEEE Access 7:32907–32921

    Article  Google Scholar 

  24. Merad-Boudia OR, Senouci SM (2020) An efficient and secure multidimensional data aggregation for fog-computing-based smart grid. IEEE Internet Things J 8(8):6143–6153

    Article  Google Scholar 

  25. Xia Z, Zhang Y, Gu K, Li X, Jia W (2021) Secure multi-dimensional and multi-angle electricity data aggregation scheme for fog computing-based smart metering system. IEEE Trans Green Commun Netw 6(1):313–28

    Article  Google Scholar 

  26. Huang C, Wang X, Gan Q, Huang D, Yao M, Lin Y (2021) A lightweight and fault-tolerable data aggregation scheme for privacy-friendly smart grids environment. Clust Comput 24(4):3495–3514

    Article  Google Scholar 

  27. Chen Y, Martínez-Ortega J-F, Castillejo P, López L (2019) An elliptic curve-based scalable data aggregation scheme for smart grid. IEEE Syst J 14(2):2066–2077

    Article  Google Scholar 

  28. Guan Z, Zhang Y, Zhu L, Longfei W, Shui Yu (2019) EFFECT: an efficient flexible privacy-preserving data aggregation scheme with authentication in smart grid. Sci China Inf Sci 62(3):1–14

    Article  Google Scholar 

  29. Jiang R, Lu R, Choo K-KR (2018) Achieving high performance and privacy-preserving query over encrypted multidimensional big metering data. Futur Gener Comput Syst 78:392–401

    Article  Google Scholar 

  30. Ben M, Raymond CK-K (2013) Cloud storage forensics: ownCloud as a case study. Digit Investig 10(4):287–299

    Article  Google Scholar 

  31. Quick Darren, Choo Kim-Kwang Raymond (2013) Forensic collection of cloud storage data: Does the act of collection result in changes to the data or its metadata? Digital Investig 10(3):266–277

    Article  Google Scholar 

  32. Paillier P (1999) Public-key cryptosystems based on composite degree residuosity classes. In: International conference on the theory and applications of cryptographic techniques, pp 223–238. Springer, Berlin, Heidelberg

  33. Smart* Data Set for Sustainability, UMass Dataset - 2017, UMassTraceRepository, 2017. http://traces.cs.umass.edu/index.php/Smart/Smart

  34. Data61, C. Python paillier library. GitHub Repository (2013). https://github.com/data61/python-paillier

Download references

Acknowledgements

The authors would like to thanks the National Institute of Technology, Kurukshetra, India, for financially supporting the research work.

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  1. Department of Computer Applications, National Institute of Technology, Kurukshetra, Haryana, 136119, India

    Ashutosh Kumar Singh & Jatinder Kumar

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  1. Ashutosh Kumar Singh
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Correspondence to Jatinder Kumar.

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Singh, A.K., Kumar, J. A privacy-preserving multidimensional data aggregation scheme with secure query processing for smart grid. J Supercomput 79, 3750–3770 (2023). https://doi.org/10.1007/s11227-022-04794-9

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